# imports
import json
import requests
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import folium
from folium.plugins import HeatMap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import math
import random 
from datetime import timedelta
import warnings
warnings.filterwarnings('ignore')

# color pallette
cnf = '#393e46'
dth = '#ff2e63'
rec = '#21bf73'
act = '#fe9801'

import plotly as py
py.offline.init_notebook_mode(connected = True)

import os

gender_c = pd.read_csv("kenya-population-by-sex-and-county.csv")
gender_c

	name	Male	Female	Intersex	Total
0	Mombasa	610257	598046	30	1208333
1	Kwale	425121	441681	18	866820
2	Kilifi	704089	749673	25	1453787
3	Tana River	158550	157391	2	315943
4	Lamu	76103	67813	4	143920
5	Taita-Taveta	173337	167327	7	340671
6	Garissa	458975	382344	34	841353
7	Wajir	415374	365840	49	781263
8	Mandera	434976	432444	37	867457
9	Marsabit	243548	216219	18	459785
10	Isiolo	139510	128483	9	268002
11	Meru	767698	777975	41	1545714
12	Tharaka-Nithi	193764	199406	7	393177
13	Embu	304208	304367	24	608599
14	Kitui	549003	587151	33	1136187
15	Machakos	710707	711191	34	1421932
16	Makueni	489691	497942	20	987653
17	Nyandarua	315022	323247	20	638289
18	Nyeri	374288	384845	31	759164
19	Kirinyaga	302011	308369	31	610411
20	Murang'a	523940	532669	31	1056640
21	Kiambu	1187146	1230454	135	2417735
22	Turkana	478087	448868	21	926976
23	West Pokot	307013	314213	15	621241
24	Samburu	156774	153546	7	310327
25	Trans Nzoia	489107	501206	28	990341
26	Uasin Gishu	580269	582889	28	1163186
27	Elgeyo-Marakwet	227317	227151	12	454480
28	Nandi	441259	444430	22	885711
29	Baringo	336322	330428	13	666763
30	Laikipia	259440	259102	18	518560
31	Nakuru	1077272	1084835	95	2162202
32	Narok	579042	578805	26	1157873
33	Kajiado	557098	560704	38	1117840
34	Kericho	450741	451008	28	901777
35	Bomet	434287	441379	23	875689
36	Kakamega	897133	970406	40	1867579
37	Vihiga	283678	306323	12	590013
38	Bungoma	812146	858389	35	1670570
39	Busia	426252	467401	28	893681
40	Siaya	471669	521496	18	993183
41	Kisumu	560942	594609	23	1155574
42	Homa Bay	539560	592367	23	1131950
43	Migori	536187	580214	35	1116436
44	Kisii	605784	661038	38	1266860
45	Nyamira	290907	314656	13	605576
46	Nairobi	2192452	2204376	245	4397073

subcounty = pd.read_csv("kenya-population-by-sub-county.csv")
subcounty.dropna(axis=1, inplace=True)
subcounty.head()

	name	Male	Female	Total
0	Mombasa	610257	598046	1208333
1	Changamwe	68761	63121	131882
2	Jomvu	83002	80410	163415
3	Kisauni	146748	145176	291930
4	Likoni	126962	123392	250358

landarea_c = pd.read_csv("kenya-population-land-area-population-density_by_county.csv")
landarea_c

	name	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Kwale	866820	8267.1	105
2	Kilifi	1453787	12539.7	116
3	Tana River	315943	37950.5	8
4	Lamu	143920	6253.3	23
5	Taita-Taveta	340671	17152.0	20
6	Garissa	841353	44736.0	19
7	Wajir	781263	56773.1	14
8	Mandera	867457	25939.8	33
9	Marsabit	459785	70944.1	6
10	Isiolo	268002	25350.6	11
11	Meru	1545714	7006.3	221
12	Tharaka-Nithi	393177	2564.4	153
13	Embu	608599	2820.7	216
14	Kitui	1136187	30429.5	37
15	Machakos	1421932	6042.7	235
16	Makueni	987653	8169.8	121
17	Nyandarua	638289	3285.7	194
18	Nyeri	759164	3325.0	228
19	Kirinyaga	610411	1478.3	413
20	Murang'a	1056640	2524.2	419
21	Kiambu	2417735	2538.6	952
22	Turkana	926976	68232.9	14
23	West Pokot	621241	9123.2	68
24	Samburu	310327	21065.1	15
25	Trans Nzoia	990341	2495.2	397
26	Uasin Gishu	1163186	3392.2	343
27	Elgeyo-Marakwet	454480	3032.0	150
28	Nandi	885711	2855.8	310
29	Baringo	666763	10976.4	61
30	Laikipia	518560	9532.2	54
31	Nakuru	2162202	7462.4	290
32	Narok	1157873	17950.3	65
33	Kajiado	1117840	21871.1	51
34	Kericho	901777	2436.1	370
35	Bomet	875689	2530.9	346
36	Kakamega	1867579	3020.0	618
37	Vihiga	590013	563.8	1047
38	Bungoma	1670570	3023.9	552
39	Busia	893681	1696.3	527
40	Siaya	993183	2529.8	393
41	Kisumu	1155574	2085.4	554
42	Homa Bay	1131950	3152.5	359
43	Migori	1116436	2613.5	427
44	Kisii	1266860	1323.0	958
45	Nyamira	605576	897.3	675
46	Nairobi	4397073	703.9	6247

landarea_sc = pd.read_csv("kenya-populationland-area-population-density_by_subcounty.csv")
landarea_sc

	National/ County	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Changamwe	131882	17.7	7457
2	Jomvu	163415	36.9	4432
3	Kisauni	291930	87.7	3328
4	Likoni	250358	40.5	6187
...	...	...	...	...
390	Makadara	189536	11.7	16150
391	Mathare	206564	3.0	68941
392	Njiru .	626482	129.9	4821
393	Starehe	210423	20.6	10205
394	Westlands	308854	97.5	3167

395 rows × 4 columns

pop_area = pd.read_csv("population-by-land-area.csv")
pop_area

	name	Year	total
0	Aberdare Fores t*	Land Area (Sq. Km)	722.0
1	Aberdare Fores t*	Population	106.0
2	Aberdare Forest	Land Area (Sq. Km)	294.7
3	Aberdare Forest	Population	43.0
4	Aberdare National Park*	Land Area (Sq. Km)	716.7
...	...	...	...
787	West Pokot	Population	621241.0
788	Westlands	Land Area (Sq. Km)	97.5
789	Westlands	Population	308854.0
790	Yatta	Land Area (Sq. Km)	1062.4
791	Yatta	Population	172583.0

792 rows × 3 columns

pop_household = pd.read_csv("population-houseshold-data.csv")
pop_household

	name	Population	No.of Households	Avg Household size
0	Mombasa	1190987	378422	31
1	Kwale	858748	173176	50
2	Kilifi	1440958	298472	48
3	Tana River	314710	68242	46
4	Lamu	141909	37963	37
5	Taita-Taveta	335747	96429	35
6	Garissa	835482	141394	59
7	Wajir	775302	127932	61
8	Mandera	862079	125763	69
9	Marsabit	447150	77495	58
10	Isiolo	267997	58072	46
11	Meru	1535635	426360	36
12	Tharaka-Nithi	391303	109860	36
13	Embu	604769	182743	33
14	Kitui	1130134	262942	43
15	Machakos	1414022	402466	35
16	Makueni	977015	244669	40
17	Nyandarua	636002	179686	35
18	Nyeri	752695	248050	30
19	Kirinyaga	605630	204188	30
20	Murang'a	1053059	318105	33
21	Kiambu	2402834	795241	30
22	Turkana	922210	164519	56
23	West Pokot	618867	116182	53
24	Samburu	307957	65910	47
25	Trans Nzoia	985333	223808	44
26	Uasin Gishu	1152671	304943	38
27	Elgeyo-Marakwet	453403	99861	45
28	Nandi	883634	199426	44
29	Baringo	662760	142518	47
30	Laikipia	513879	149271	34
31	Nakuru	2142667	616046	35
32	Narok	1149379	241125	48
33	Kajiado	1107296	316179	35
34	Kericho	896863	206036	44
35	Bomet	873023	187641	47
36	Kakamega	1861332	433207	43
37	Vihiga	587189	143365	41
38	Bungoma	1663898	358796	46
39	Busia	886856	198152	45
40	Siaya	989708	250698	39
41	Kisumu	1144777	300745	38
42	Homa Bay	1125823	262036	43
43	Migori	1108950	240168	46
44	Kisii	1260509	308054	41
45	Nyamira	603051	150669	40
46	Nairobi	4337080	1506888	29

pop_household.head(2)

	name	Population	No.of Households	Avg Household size
0	Mombasa	1190987	378422	31
1	Kwale	858748	173176	50

Population and NO of Households in Counties

fig_p = px.bar(pop_household, x='name', y='Population', color_discrete_sequence=[act])
fig_h = px.bar(pop_household, x='name', y='No.of Households', color_discrete_sequence=[dth])

fig = make_subplots(rows = 1, cols = 2, shared_xaxes=False, horizontal_spacing=0.1,
                   subplot_titles=('Population', 'No.of Households'))
fig.add_trace(fig_p['data'][0], row=1, col = 1)
fig.add_trace(fig_h['data'][0], row=1, col = 2)

fig.update_layout(height=500)
fig.show()

landarea_c.head(2)

	name	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Kwale	866820	8267.1	105

Population Land area and population density per sq km lineplot

fig1 = px.line(landarea_c, x='name', y='Population', color_discrete_sequence=[dth])
fig2 = px.line(landarea_c, x='name', y='Land Area (Sq. Km)', color_discrete_sequence=[rec])
fig3 = px.line(landarea_c, x='name', y='Population Density (No. per Sq. Km)', color_discrete_sequence=[act])

fig = make_subplots(rows = 1, cols = 3, shared_xaxes=False,
                   subplot_titles=('Population', 'Land Area (Sq. Km)', 'Population Density (No. per Sq. Km)'))

fig.add_trace(fig1['data'][0], row = 1, col = 1)
fig.add_trace(fig2['data'][0], row = 1, col = 2)
fig.add_trace(fig3['data'][0], row = 1, col = 3)

fig.update_layout(height = 400)
fig.show()

landarea_sc.head(2)

	National/ County	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Changamwe	131882	17.7	7457

Top 10 analysis

top = 10

fig_p = px.bar(landarea_sc.sort_values('Population', ascending=False).head(top), x='Population', y='National/ County',
              text='Population', orientation='h', color_discrete_sequence=[cnf])

fig_l = px.bar(landarea_sc.sort_values('Land Area (Sq. Km)', ascending=False).head(top), x='Land Area (Sq. Km)', y='National/ County',
              text='Land Area (Sq. Km)', orientation='h', color_discrete_sequence=[act])

fig_d = px.bar(landarea_sc.sort_values('Population Density (No. per Sq. Km)', ascending=False).head(top), x='Population Density (No. per Sq. Km)', y='National/ County',
              text='Population Density (No. per Sq. Km)', orientation='h', color_discrete_sequence=[act])

fig = make_subplots(rows=3, cols=1, shared_xaxes=False, vertical_spacing=0.05,
                   subplot_titles=("Population", "Land Area (Sq. Km)", "Population Density (No. per Sq. Km)"))

fig.add_trace(fig_p['data'][0], row = 1, col = 1)
fig.add_trace(fig_l['data'][0], row = 2, col = 1)
fig.add_trace(fig_d['data'][0], row = 3, col = 1)

fig.update_layout(height=2000)
fig.show()

gender_c.head(2)

	name	Male	Female	Intersex	Total
0	Mombasa	610257	598046	30	1208333
1	Kwale	425121	441681	18	866820

Scatter plot for female vs Male

top =15
fig = px.scatter(gender_c.sort_values('Female', ascending=False).head(top),
                x='Male', y='Female', color='name', size='Male', height=700,
                text='name', log_x=True, log_y=True, title="male vs female")

fig.update_traces(textposition='top center')
fig.update_layout(showlegend=True)
fig.update_layout(xaxis_rangeslider_visible=True)
fig.show()

landarea_sc.head(2)

	National/ County	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Changamwe	131882	17.7	7457

Population, No.of Household and avg household size

fig = px.treemap(landarea_sc.sort_values(by='Land Area (Sq. Km)', ascending=False),
                path=['National/ County', 'Population Density (No. per Sq. Km)'], values='Land Area (Sq. Km)', height=700,
                title='Land Area (Sq. Km)',
                color_discrete_sequence = px.colors.qualitative.Dark2)
fig.data[0].textinfo='label+text+value'
fig.show()

df = pd.read_csv('population.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
df['County'].replace({'Taita–Taveta':'Taita-Taveta', 'Trans-Nzoia':'Trans Nzoia', 'Nairobi (County)':'Nairobi'},inplace=True)
df

	Code	County	FormerProvince	Area(km2)	Population(2009 Census)	Population(2019 Census)	Capital
0	1.0	Mombasa	Coast	212.5	939370	1208333	Mombasa (City)
1	2.0	Kwale	Coast	8270.3	649931	866820	Kwale
2	3.0	Kilifi	Coast	12245.9	1109735	1453787	Kilifi
3	4.0	Tana River	Coast	35375.8	240075	315943	Hola
4	5.0	Lamu	Coast	6497.7	191539	143920	Lamu
5	6.0	Taita-Taveta	Coast	17083.9	284657	340671	Mwatate
6	7.0	Garissa	North Eastern	45720.2	623060	841353	Garissa
7	8.0	Wajir	North Eastern	55840.6	661941	781263	Wajir
8	9.0	Mandera	North Eastern	25797.7	1025756	867457	Mandera
9	10.0	Marsabit	Eastern	66923.1	291166	459785	Marsabit
10	11.0	Isiolo	Eastern	25336.1	143294	268002	Isiolo
11	12.0	Meru	Eastern	7003.1	1356301	1545714	Meru
12	13.0	Tharaka-Nithi	Eastern	2609.5	365330	393177	Kathwana
13	14.0	Embu	Eastern	2555.9	516212	608599	Embu
14	15.0	Kitui	Eastern	24385.1	1012709	1136187	Kitui
15	16.0	Machakos	Eastern	5952.9	1098584	1421932	Machakos
16	17.0	Makueni	Eastern	8008.9	884527	987653	Wote
17	18.0	Nyandarua	Central	3107.7	596268	638289	Ol Kalou
18	19.0	Nyeri	Central	2361.0	693558	759164	Nyeri
19	20.0	Kirinyaga	Central	1205.4	528054	610411	Kerugoya / Kutus
20	21.0	Murang'a	Central	2325.8	942581	1056640	Murang'a
21	22.0	Kiambu	Central	2449.2	1623282	2417735	Kiambu
22	23.0	Turkana	Rift Valley	71597.8	855399	926976	Lodwar
23	24.0	West Pokot	Rift Valley	8418.2	512690	621241	Kapenguria
24	25.0	Samburu	Rift Valley	20182.5	223947	310327	Maralal
25	26.0	Trans Nzoia	Rift Valley	2469.9	818757	990341	Kitale
26	27.0	Uasin Gishu	Rift Valley	2955.3	894179	1163186	Eldoret
27	28.0	Elgeyo-Marakwet	Rift Valley	3049.7	369998	454480	Iten
28	29.0	Nandi	Rift Valley	2884.5	752965	885711	Kapsabet
29	30.0	Baringo	Rift Valley	11075.3	555561	666763	Kabarnet
30	31.0	Laikipia	Rift Valley	8696.1	399227	518560	Rumuruti
31	32.0	Nakuru	Rift Valley	7509.5	1603325	2162202	Nakuru (City)[8][9]
32	33.0	Narok	Rift Valley	17921.2	850920	1157873	Narok
33	34.0	Kajiado	Rift Valley	21292.7	687312	1117840	Kajiado
34	35.0	Kericho	Rift Valley	2454.5	752396	901777	Kericho
35	36.0	Bomet	Rift Valley	1997.9	730129	875689	Bomet
36	37.0	Kakamega	Western	3033.8	1660651	1867579	Kakamega
37	38.0	Vihiga	Western	531.3	554622	590013	Vihiga
38	39.0	Bungoma	Western	2206.9	1375063	1670570	Bungoma
39	40.0	Busia	Western	1628.4	743946	893681	Busia
40	41.0	Siaya	Nyanza	2496.1	842304	993183	Siaya
41	42.0	Kisumu	Nyanza	2009.5	968909	1155574	Kisumu(City)
42	43.0	Homa Bay	Nyanza	3154.7	963794	1131950	Homa Bay
43	44.0	Migori	Nyanza	2586.4	917170	1116436	Migori
44	45.0	Kisii	Nyanza	1317.9	1152282	1266860	Kisii
45	46.0	Nyamira	Nyanza	912.5	598252	605576	Nyamira
46	47.0	Nairobi	Nairobi (Province)	694.9	3138369	4397073	Nairobi (City)

2019 population census with a sunburst

px.sunburst(df, path=['FormerProvince','County'], values='Population(2019 Census)', hover_name='County', 
            color='Area(km2)', height=700, title='Population Census 2019')

2019 population census with a bar chart

px.bar(df, x='FormerProvince', y='Population(2019 Census)', hover_name='County', color='FormerProvince', height=700,
      title='2019 population census with a bar chart')

landarea_c.head(2)

	name	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)
0	Mombasa	1208333	219.9	5495
1	Kwale	866820	8267.1	105

counties = json.load(open("kenyan-counties.geojson.txt", 'r'))
counties['features'][0]

{'type': 'Feature',
 'properties': {'OBJECTID': 1,
  'AREA': 5.677,
  'PERIMETER': 15.047,
  'COUNTY3_': 2,
  'COUNTY3_ID': 1,
  'COUNTY': 'Turkana',
  'county_id': 43,
  'Shape_Leng': 15.0468376942,
  'Shape_Area': 5.67698507035},
 'geometry': {'type': 'Polygon',
  'coordinates': [[[35.795925139933445, 5.344485759410418],
    [35.796592712084134, 5.344676018183861],
    [35.79743194592771, 5.345106125247639],
    [35.79821014368338, 5.345294952300378],
    [35.79901885993263, 5.345104217785604],
    [35.80001831069666, 5.344961642865712],
    [35.801631927371716, 5.344852924722886],
    [35.802825927574645, 5.345104217785604],
    [35.80353927611685, 5.345675468048569],
    [35.80411148056055, 5.346293926423755],
    [35.80472946139571, 5.346817493732487],
    [35.80496597320126, 5.347007751606611],
    [35.805488586329375, 5.347103119313658],
    [35.80596542396529, 5.346865177586039],
    [35.80639648431048, 5.346293926423755],
    [35.80677795423935, 5.345124721428931],
    [35.80744171146597, 5.34400987595518],
    [35.80801391590967, 5.343390941838663],
    [35.808727264451875, 5.343010425191153],
    [35.80977249160736, 5.342867851170581],
    [35.81067657466434, 5.343058109044648],
    [35.8124847407783, 5.343153000111045],
    [35.81405639688535, 5.343010425191153],
    [35.81548309307044, 5.342724800509302],
    [35.81695938147044, 5.342439651568839],
    [35.818161010622305, 5.342112064520336],
    [35.81900405849069, 5.341630458678878],
    [35.819530487442194, 5.341011524562362],
    [35.81967163064138, 5.340202808313109],
    [35.819530487442194, 5.339393615423148],
    [35.819480896126606, 5.338346481704946],
    [35.81990814244699, 5.33720445602097],
    [35.82057571369842, 5.336442946984562],
    [35.821529388070985, 5.335634231634629],
    [35.8223838807117, 5.335206032032886],
    [35.823623657306086, 5.335110664325839],
    [35.82481002766082, 5.335348606053515],
    [35.82657241828264, 5.335729121801705],
    [35.82833480800514, 5.335919857215799],
    [35.83042526231617, 5.336014747382819],
    [35.831951141132436, 5.335872173362247],
    [35.83332824690126, 5.335538863927582],
    [35.83456802349565, 5.335062980472344],
    [35.83580398516597, 5.33449220685003],
    [35.837043761760356, 5.334158897415364],
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    ...]]}}

#import webbrowser
#urL='https://www.google.com'
#firefox_path="C:\\Program Files\\Mozilla Firefox\\firefox.exe"
#webbrowser.register('firefox', None,webbrowser.BackgroundBrowser(firefox_path))
#webbrowser.get('firefox').open_new_tab(urL)

counties_map = {}
for feature in counties['features']:
    feature['id'] = feature['properties']['COUNTY3_']
    counties_map[feature['properties']['COUNTY']] = feature['id']
    

counties_map

{'Turkana': 2,
 'Marsabit': 3,
 'Mandera': 4,
 'Wajir': 5,
 'West Pokot': 6,
 'Samburu': 7,
 'Isiolo': 8,
 'Baringo': 9,
 'Elgeyo-Marakwet': 10,
 'Trans Nzoia': 11,
 'Bungoma': 12,
 'Garissa': 13,
 'Uasin Gishu': 14,
 'Kakamega': 15,
 'Laikipia': 16,
 'Busia': 17,
 'Meru': 18,
 'Nandi': 19,
 'Siaya': 20,
 'Nakuru': 21,
 'Vihiga': 22,
 'Nyandarua': 23,
 'Tharaka-Nithi': 24,
 'Kericho': 25,
 'Kisumu': 26,
 'Nyeri': 27,
 'Tana River': 28,
 'Kitui': 29,
 'Kirinyaga': 30,
 'Embu': 31,
 'Homa Bay': 32,
 'Bomet': 33,
 'Nyamira': 34,
 'Narok': 35,
 'Kisii': 36,
 "Murang'a": 37,
 'Migori': 38,
 'Kiambu': 39,
 'Machakos': 40,
 'Kajiado': 41,
 'Nairobi': 42,
 'Makueni': 43,
 'Lamu': 44,
 'Kilifi': 111,
 'Taita-Taveta': 112,
 'Kwale': 136,
 'Mombasa': 142}

landarea_c['id'] = landarea_c['name'].map(counties_map)
landarea_c

	name	Population	Land Area (Sq. Km)	Population Density (No. per Sq. Km)	id
0	Mombasa	1208333	219.9	5495	142
1	Kwale	866820	8267.1	105	136
2	Kilifi	1453787	12539.7	116	111
3	Tana River	315943	37950.5	8	28
4	Lamu	143920	6253.3	23	44
5	Taita-Taveta	340671	17152.0	20	112
6	Garissa	841353	44736.0	19	13
7	Wajir	781263	56773.1	14	5
8	Mandera	867457	25939.8	33	4
9	Marsabit	459785	70944.1	6	3
10	Isiolo	268002	25350.6	11	8
11	Meru	1545714	7006.3	221	18
12	Tharaka-Nithi	393177	2564.4	153	24
13	Embu	608599	2820.7	216	31
14	Kitui	1136187	30429.5	37	29
15	Machakos	1421932	6042.7	235	40
16	Makueni	987653	8169.8	121	43
17	Nyandarua	638289	3285.7	194	23
18	Nyeri	759164	3325.0	228	27
19	Kirinyaga	610411	1478.3	413	30
20	Murang'a	1056640	2524.2	419	37
21	Kiambu	2417735	2538.6	952	39
22	Turkana	926976	68232.9	14	2
23	West Pokot	621241	9123.2	68	6
24	Samburu	310327	21065.1	15	7
25	Trans Nzoia	990341	2495.2	397	11
26	Uasin Gishu	1163186	3392.2	343	14
27	Elgeyo-Marakwet	454480	3032.0	150	10
28	Nandi	885711	2855.8	310	19
29	Baringo	666763	10976.4	61	9
30	Laikipia	518560	9532.2	54	16
31	Nakuru	2162202	7462.4	290	21
32	Narok	1157873	17950.3	65	35
33	Kajiado	1117840	21871.1	51	41
34	Kericho	901777	2436.1	370	25
35	Bomet	875689	2530.9	346	33
36	Kakamega	1867579	3020.0	618	15
37	Vihiga	590013	563.8	1047	22
38	Bungoma	1670570	3023.9	552	12
39	Busia	893681	1696.3	527	17
40	Siaya	993183	2529.8	393	20
41	Kisumu	1155574	2085.4	554	26
42	Homa Bay	1131950	3152.5	359	32
43	Migori	1116436	2613.5	427	38
44	Kisii	1266860	1323.0	958	36
45	Nyamira	605576	897.3	675	34
46	Nairobi	4397073	703.9	6247	42

Landarea in sq.km of Kenyan Counties

fig = px.choropleth(landarea_c, 
              locations='id', 
              geojson=counties, 
              color='Land Area (Sq. Km)', 
              scope='africa', 
              hover_name='name', 
              hover_data=['Population Density (No. per Sq. Km)'],
              title='Landarea of Counties in square km',
              height = 700)
fig.update_geos(fitbounds="locations", visible=False)
fig.show()

df.head(2)

	Code	County	FormerProvince	Area(km2)	Population(2009 Census)	Population(2019 Census)	Capital
0	1.0	Mombasa	Coast	212.5	939370	1208333	Mombasa (City)
1	2.0	Kwale	Coast	8270.3	649931	866820	Kwale

df['id'] = df['County'].map(counties_map)
df

	Code	County	FormerProvince	Area(km2)	Population(2009 Census)	Population(2019 Census)	Capital	id
0	1.0	Mombasa	Coast	212.5	939370	1208333	Mombasa (City)	142
1	2.0	Kwale	Coast	8270.3	649931	866820	Kwale	136
2	3.0	Kilifi	Coast	12245.9	1109735	1453787	Kilifi	111
3	4.0	Tana River	Coast	35375.8	240075	315943	Hola	28
4	5.0	Lamu	Coast	6497.7	191539	143920	Lamu	44
5	6.0	Taita-Taveta	Coast	17083.9	284657	340671	Mwatate	112
6	7.0	Garissa	North Eastern	45720.2	623060	841353	Garissa	13
7	8.0	Wajir	North Eastern	55840.6	661941	781263	Wajir	5
8	9.0	Mandera	North Eastern	25797.7	1025756	867457	Mandera	4
9	10.0	Marsabit	Eastern	66923.1	291166	459785	Marsabit	3
10	11.0	Isiolo	Eastern	25336.1	143294	268002	Isiolo	8
11	12.0	Meru	Eastern	7003.1	1356301	1545714	Meru	18
12	13.0	Tharaka-Nithi	Eastern	2609.5	365330	393177	Kathwana	24
13	14.0	Embu	Eastern	2555.9	516212	608599	Embu	31
14	15.0	Kitui	Eastern	24385.1	1012709	1136187	Kitui	29
15	16.0	Machakos	Eastern	5952.9	1098584	1421932	Machakos	40
16	17.0	Makueni	Eastern	8008.9	884527	987653	Wote	43
17	18.0	Nyandarua	Central	3107.7	596268	638289	Ol Kalou	23
18	19.0	Nyeri	Central	2361.0	693558	759164	Nyeri	27
19	20.0	Kirinyaga	Central	1205.4	528054	610411	Kerugoya / Kutus	30
20	21.0	Murang'a	Central	2325.8	942581	1056640	Murang'a	37
21	22.0	Kiambu	Central	2449.2	1623282	2417735	Kiambu	39
22	23.0	Turkana	Rift Valley	71597.8	855399	926976	Lodwar	2
23	24.0	West Pokot	Rift Valley	8418.2	512690	621241	Kapenguria	6
24	25.0	Samburu	Rift Valley	20182.5	223947	310327	Maralal	7
25	26.0	Trans Nzoia	Rift Valley	2469.9	818757	990341	Kitale	11
26	27.0	Uasin Gishu	Rift Valley	2955.3	894179	1163186	Eldoret	14
27	28.0	Elgeyo-Marakwet	Rift Valley	3049.7	369998	454480	Iten	10
28	29.0	Nandi	Rift Valley	2884.5	752965	885711	Kapsabet	19
29	30.0	Baringo	Rift Valley	11075.3	555561	666763	Kabarnet	9
30	31.0	Laikipia	Rift Valley	8696.1	399227	518560	Rumuruti	16
31	32.0	Nakuru	Rift Valley	7509.5	1603325	2162202	Nakuru (City)[8][9]	21
32	33.0	Narok	Rift Valley	17921.2	850920	1157873	Narok	35
33	34.0	Kajiado	Rift Valley	21292.7	687312	1117840	Kajiado	41
34	35.0	Kericho	Rift Valley	2454.5	752396	901777	Kericho	25
35	36.0	Bomet	Rift Valley	1997.9	730129	875689	Bomet	33
36	37.0	Kakamega	Western	3033.8	1660651	1867579	Kakamega	15
37	38.0	Vihiga	Western	531.3	554622	590013	Vihiga	22
38	39.0	Bungoma	Western	2206.9	1375063	1670570	Bungoma	12
39	40.0	Busia	Western	1628.4	743946	893681	Busia	17
40	41.0	Siaya	Nyanza	2496.1	842304	993183	Siaya	20
41	42.0	Kisumu	Nyanza	2009.5	968909	1155574	Kisumu(City)	26
42	43.0	Homa Bay	Nyanza	3154.7	963794	1131950	Homa Bay	32
43	44.0	Migori	Nyanza	2586.4	917170	1116436	Migori	38
44	45.0	Kisii	Nyanza	1317.9	1152282	1266860	Kisii	36
45	46.0	Nyamira	Nyanza	912.5	598252	605576	Nyamira	34
46	47.0	Nairobi	Nairobi (Province)	694.9	3138369	4397073	Nairobi (City)	42

2019 population census of kenyan counties

fig = px.choropleth(df, 
              locations='id', 
              geojson=counties, 
              color='Population(2019 Census)', 
              scope='africa', 
              hover_name='County', 
              hover_data=['Area(km2)', 'Population(2009 Census)'],
              title='2019 population census',
              height = 700)
fig.update_geos(fitbounds="locations", visible=False)
fig.show()